The present invention relates to efficiently extracting features from images, and in particular to automatically recognizing settlements in the underdeveloped world from satellite images.
Mapping human settlements and transportation networks in developing countries is critical to the successful planning and execution of global development and health programs. Detailed settlement maps support logistics and planning such as needed for the delivery of vaccines to rural areas and also create a foundation for development of more accurate population estimates critical to health programs.
Mapping human infrastructure in detail, including small settlements, compounds and local transportation networks using satellite imagery has been a labor intensive endeavor that requires highly skilled image analysts. Manual settlement extraction is also subject to certain analyst bias and therefore is not necessarily a repeatable process. Advancing the state of the science in automated techniques to capture the detailed features is the key to cost, time and workforce savings for governments of developing nations.
In image analysis, whether for satellite images or other images, there are many methods for feature identification. A first step in many methods is to break up the image into segments. Standard segmentation workflows may result in segments derived from rectangular grids, or superpixels. Superpixels are groupings of pixels that are similar insame spectral characteristics (color or shade), and/or being in proximity to each other. There are a number of algorithms for generating superpixels. The invention as described below in one embodiment adapts an existing algorithm, Simple Linear Iterative Clustering (SLIC), as described in “SLIC Superpixels Compared to State-of-the-art Superpixel Methods,” by Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, DECEMBER 2011. SLIC uses k-means clustering, which basically clusters similar pixels or superpixels based on how close they are to each other.